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CodeSearch-ModernBERT-Owl-v1

Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:3999600
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files
xet
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SentenceTransformer based on Shuu12121/CodeModernBERT-Owl-v1

This is a sentence-transformers model finetuned from Shuu12121/CodeModernBERT-Owl-v1. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: Shuu12121/CodeModernBERT-Owl-v1
  • Maximum Sequence Length: 1024 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    '#\nDeletes the cluster, including the Kubernetes endpoint and all worker\nnodes.\n\nFirewalls and routes that were configured during cluster creation\nare also deleted.\n\nOther Google Compute Engine resources that might be in use by the cluster,\nsuch as load balancer resources, are not deleted if they weren\'t present\nwhen the cluster was initially created.\n\n@overload delete_cluster(request, options = nil)\nPass arguments to `delete_cluster` via a request object, either of type\n{::Google::Cloud::Container::V1::DeleteClusterRequest} or an equivalent Hash.\n\n@param request [::Google::Cloud::Container::V1::DeleteClusterRequest, ::Hash]\nA request object representing the call parameters. Required. To specify no\nparameters, or to keep all the default parameter values, pass an empty Hash.\n@param options [::Gapic::CallOptions, ::Hash]\nOverrides the default settings for this call, e.g, timeout, retries, etc. Optional.\n\n@overload delete_cluster(project_id: nil, zone: nil, cluster_id: nil, name: nil)\nPass arguments to `delete_cluster` via keyword arguments. Note that at\nleast one keyword argument is required. To specify no parameters, or to keep all\nthe default parameter values, pass an empty Hash as a request object (see above).\n\n@param project_id [::String]\nDeprecated. The Google Developers Console [project ID or project\nnumber](https://cloud.google.com/resource-manager/docs/creating-managing-projects).\nThis field has been deprecated and replaced by the name field.\n@param zone [::String]\nDeprecated. The name of the Google Compute Engine\n[zone](https://cloud.google.com/compute/docs/zones#available) in which the\ncluster resides. This field has been deprecated and replaced by the name\nfield.\n@param cluster_id [::String]\nDeprecated. The name of the cluster to delete.\nThis field has been deprecated and replaced by the name field.\n@param name [::String]\nThe name (project, location, cluster) of the cluster to delete.\nSpecified in the format `projects/*/locations/*/clusters/*`.\n\n@yield [response, operation] Access the result along with the RPC operation\n@yieldparam response [::Google::Cloud::Container::V1::Operation]\n@yieldparam operation [::GRPC::ActiveCall::Operation]\n\n@return [::Google::Cloud::Container::V1::Operation]\n\n@raise [::Google::Cloud::Error] if the RPC is aborted.\n\n@example Basic example\nrequire "google/cloud/container/v1"\n\n# Create a client object. The client can be reused for multiple calls.\nclient = Google::Cloud::Container::V1::ClusterManager::Client.new\n\n# Create a request. To set request fields, pass in keyword arguments.\nrequest = Google::Cloud::Container::V1::DeleteClusterRequest.new\n\n# Call the delete_cluster method.\nresult = client.delete_cluster request\n\n# The returned object is of type Google::Cloud::Container::V1::Operation.\np result',
    'def delete_cluster request, options = nil\n              raise ::ArgumentError, "request must be provided" if request.nil?\n\n              request = ::Gapic::Protobuf.coerce request, to: ::Google::Cloud::Container::V1::DeleteClusterRequest\n\n              # Converts hash and nil to an options object\n              options = ::Gapic::CallOptions.new(**options.to_h) if options.respond_to? :to_h\n\n              # Customize the options with defaults\n              metadata = @config.rpcs.delete_cluster.metadata.to_h\n\n              # Set x-goog-api-client, x-goog-user-project and x-goog-api-version headers\n              metadata[:"x-goog-api-client"] ||= ::Gapic::Headers.x_goog_api_client \\\n                lib_name: @config.lib_name, lib_version: @config.lib_version,\n                gapic_version: ::Google::Cloud::Container::V1::VERSION\n              metadata[:"x-goog-api-version"] = API_VERSION unless API_VERSION.empty?\n              metadata[:"x-goog-user-project"] = @quota_project_id if @quota_project_id\n\n              header_params = {}\n              if request.name\n                header_params["name"] = request.name\n              end\n\n              request_params_header = header_params.map { |k, v| "#{k}=#{v}" }.join("&")\n              metadata[:"x-goog-request-params"] ||= request_params_header\n\n              options.apply_defaults timeout:      @config.rpcs.delete_cluster.timeout,\n                                     metadata:     metadata,\n                                     retry_policy: @config.rpcs.delete_cluster.retry_policy\n\n              options.apply_defaults timeout:      @config.timeout,\n                                     metadata:     @config.metadata,\n                                     retry_policy: @config.retry_policy\n\n              @cluster_manager_stub.call_rpc :delete_cluster, request, options: options do |response, operation|\n                yield response, operation if block_given?\n              end\n            rescue ::GRPC::BadStatus => e\n              raise ::Google::Cloud::Error.from_error(e)\n            end',
    'device(deviceType, deviceId = 0) {\n\t        return new DLDevice(deviceType, deviceId, this.lib);\n\t    }',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 3,999,600 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 label
    type string string float
    details
    • min: 8 tokens
    • mean: 74.13 tokens
    • max: 1024 tokens
    • min: 13 tokens
    • mean: 154.33 tokens
    • max: 1024 tokens
    • min: 1.0
    • mean: 1.0
    • max: 1.0
  • Samples:
    sentence_0 sentence_1 label
    Set the column title

    @param column - column number (first column is: 0)
    @param title - new column title    		 setHeader = function(column, newValue) {
    const obj = this;

    if (obj.headers[column]) {
    const oldValue = obj.headers[column].textContent;
    const onchangeheaderOldValue = (obj.options.columns && obj.options.columns[column] && obj.options.columns[column].title)
    Elsewhere this is known as a "Weak Value Map". Whereas a std JS WeakMap
    is weak on its keys, this map is weak on its values. It does not retain these
    values strongly. If a given value disappears, then the entries for it
    disappear from every weak-value-map that holds it as a value.

    Just as a WeakMap only allows gc-able values as keys, a weak-value-map
    only allows gc-able values as values.

    Unlike a WeakMap, a weak-value-map unavoidably exposes the non-determinism of
    gc to its clients. Thus, both the ability to create one, as well as each
    created one, must be treated as dangerous capabilities that must be closely
    held. A program with access to these can read side channels though gc that do
    not* rely on the ability to measure duration. This is a separate, and bad,
    timing-independent side channel.

    This non-determinism also enables code to escape deterministic replay. In a
    blockchain context, this could cause validators to differ from each other,
    preventing consensus, and thus preventing ...    		 makeFinalizingMap = (finalizer, opts) => {
    const { weakValues = false } = opts
    Creates a function that memoizes the result of func. If resolver is
    provided, it determines the cache key for storing the result based on the
    arguments provided to the memoized function. By default, the first argument
    provided to the memoized function is used as the map cache key. The func
    is invoked with the this binding of the memoized function.

    Note: The cache is exposed as the cache property on the memoized
    function. Its creation may be customized by replacing the _.memoize.Cache
    constructor with one whose instances implement the
    Map
    method interface of delete, get, has, and set.

    @static
    @memberOf _
    @since 0.1.0
    @category Function
    @param {Function} func The function to have its output memoized.
    @param {Function} [resolver] The function to resolve the cache key.
    @returns {Function} Returns the new memoized function.
    @example

    var object = { 'a': 1, 'b': 2 };
    var othe...    		 function memoize(func, resolver) {
    if (typeof func != 'function'
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 150
  • per_device_eval_batch_size: 150
  • num_train_epochs: 1
  • fp16: True
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 150
  • per_device_eval_batch_size: 150
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 1
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: True
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • hub_revision: None
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin

Training Logs

Epoch Step Training Loss
0.0188 500 0.2957
0.0375 1000 0.1174
0.0563 1500 0.1148
0.0750 2000 0.104
0.0938 2500 0.0977
0.1125 3000 0.0944
0.1313 3500 0.0885
0.1500 4000 0.083
0.1688 4500 0.0817
0.1875 5000 0.077
0.2063 5500 0.0764
0.2250 6000 0.0725
0.2438 6500 0.0698
0.2625 7000 0.0663
0.2813 7500 0.0644
0.3000 8000 0.0606
0.3188 8500 0.0587
0.3375 9000 0.0596
0.3563 9500 0.0566
0.3750 10000 0.0536
0.3938 10500 0.0514
0.4125 11000 0.0532
0.4313 11500 0.0501
0.4500 12000 0.0478
0.4688 12500 0.0483
0.4875 13000 0.0461
0.5063 13500 0.0444
0.5251 14000 0.0443
0.5438 14500 0.0402
0.5626 15000 0.0417
0.5813 15500 0.0386
0.6001 16000 0.0421
0.6188 16500 0.0368
0.6376 17000 0.036
0.6563 17500 0.0352
0.6751 18000 0.0339
0.6938 18500 0.0336
0.7126 19000 0.0334
0.7313 19500 0.0312
0.7501 20000 0.0325
0.7688 20500 0.0317
0.7876 21000 0.0284
0.8063 21500 0.0281
0.8251 22000 0.0294
0.8438 22500 0.0283
0.8626 23000 0.0277
0.8813 23500 0.0268
0.9001 24000 0.0254
0.9188 24500 0.0249
0.9376 25000 0.0255
0.9563 25500 0.0251
0.9751 26000 0.0244
0.9938 26500 0.0249

Framework Versions

  • Python: 3.11.13
  • Sentence Transformers: 4.1.0
  • Transformers: 4.53.2
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.9.0
  • Datasets: 3.6.0
  • Tokenizers: 0.21.2

Citation

BibTeX

Sentence Transformers 

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss 

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
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